Model obsolescence is a major impediment to the success of the deployment of analytic models and this is particularly the case for mission-critical applications. The rate of obsolescence might vary depending on the application and the dynamics involved. Usually, model performance may deteriorate drastically within a year from the initial deployment thereof, if model maintenance is not applied. This may also create a lack of confidence in the aging models. In large part, the existing approach to model maintenance is a manual process. This prevents achieving scalability in the size of the data, number of models, and maintaining consistent model performance.